Building RAG Applications with LangChain: A Production-Ready Guide

Fine-tuning is expensive, slow, and usually overkill. Retrieval-Augmented Generation (RAG) lets your LLM answer questions about your data without touching model weights — and you can have a working prototype in an afternoon. But "working prototype" and "production system" are very different things. The gap between a demo that retrieves something and a pipeline that retrieves the right thing with good latency and manageable cost is where most teams get stuck. This guide bridges that gap. Architecture Overview A RAG pipeline has two phases: Indexing (offline): Load documents from various sources Split into semantically meaningful chunks Generate embedding vectors Store in a vector database Retrieval + Generation (runtime): User asks a question Embed the question using the same model Search the vector store for similar chunks Feed chunks + question to the LLM Return the generated answer with source attribution ┌─────────┐ ┌──────────┐ ┌────────────┐ ┌──────────┐ │Documents│───>│ Chunki